(self, x, return_feature_maps=False)
| 154 | m.padding = (dilate, dilate) |
| 155 | |
| 156 | def forward(self, x, return_feature_maps=False): |
| 157 | conv_out = [] |
| 158 | |
| 159 | x = self.relu1(self.bn1(self.conv1(x))) |
| 160 | x = self.relu2(self.bn2(self.conv2(x))) |
| 161 | x = self.relu3(self.bn3(self.conv3(x))) |
| 162 | x = self.maxpool(x) |
| 163 | |
| 164 | x = self.layer1(x); conv_out.append(x); |
| 165 | x = self.layer2(x); conv_out.append(x); |
| 166 | x = self.layer3(x); conv_out.append(x); |
| 167 | x = self.layer4(x); conv_out.append(x); |
| 168 | |
| 169 | if return_feature_maps: |
| 170 | return conv_out |
| 171 | return [x] |
| 172 | |
| 173 | |
| 174 | # pyramid pooling, deep supervision |
nothing calls this directly
no outgoing calls
no test coverage detected